The Research on Social Networks Public Opinion Propagation Influence Models and Its Controllability

Public opinion propagation control is one of the hot topics in contemporary social network research. With the rapid dissemination of information over the Internet, the traditional isolation and vaccination strategies can no longer achieve satisfactory results. A positive guidance technology for public opinion diffusion is urgently needed. First, based on the analysis of influence network controllability and public opinion diffusion, a positive guidance technology is proposed and a new model that supports external control is established. Second, in combination with the influence network, a public opinion propagation influence network model is designed and a public opinion control point selection algorithm(POCDNSA) is proposed. Finally, An experiment verified that this algorithm can lead to users receiving the correct guidance quickly and accurately, reducing the impact of false public opinion information; the effect of CELF is no better than that of the POCDNSA algorithm. The main reason is that the former is completely based on the diffusion cascade information contained in the training data, but does not consider the specific situation of the network structure and the diffusion of public opinion information in the closed set. thus, the effectiveness and feasibility of the algorithm is proven. The findings of this article therefore provide useful insights for the implementation of public opinion control.

The Study of SINA micro-blog Opinion Leaders Effect on Network Public Opinion Transmission

Opinion leaders play a critical role in network public opinion transmission, their perspectives can shape public opinion and influence policy formulation and implementation. The paper is based on SINA micro-blog, by structural equation model, as Fudan Poisoning Event for example, On the basis of in-depth analysis of opinion leaders effect on network public opinion transmission characteristics, Explore the opinion leaders on the influence of network public opinion transmission mechanism, in order to better play a role of opinion leader＇s guidance of public opinion.

Research on Public Opinion Transmission and Evolution Mechanism in Network

As the Internet continues to expand, the influence of network information on real life has gradually deepened. Research on propagation and evolution of Internet public opinion has become a hot topic. The force of intemet public opinion penetrates and influences on every aspect of the society. Compared with traditional public opinion, the network public opinion has features of immediate, multivariate and interactive, the propagation behavior has the new change compared with the traditional public opinion. In the propagation behavior of network public opinion, agenda setting is no longer an arbitrary, the influence of opinion leaders in the agenda setting becomes more and more complex and diversified. The formation time of network public opinion is short, and social influence becomes strong. Guide the public opinion to build a harmonious environment of network public opinion. Overall, our country＇ s network public opinion environment is a benign situation and steadily promoting the reforms of public policies. Although there is still a few not rational voices full of them, the network public opinion shows a general trend of positive thinking. Based on this philosophy, through the research of network public opinion dissemination and evolution mechanism, can be all-round good guidance and supervision of public opinion, building a harmonious network environment of public opinion.

Research on crisis management of enterprise network public opinion

With the arrival ofthe era from the media, the internet spread over all sectors ofsocicty, affect people＇s political, economic and cultural life. The network has become the main place, people express their views so also. And the popularity of media enables consumers to easily express their products, services and other aspects of the topic, spread on the network at breakneck speed, and other users of the attention and support, thus forming a kind of public opinion, influence The development of enterprises, which will undoubtedly bring unprecedented pressure, constant attention, coupled with people＇s comments, forwarding, dissemination media, making the crisis spread, enterprises should not reasonable, effective, will enable enterprises to establish a sense of trust collapsed, brought a negative impact to the enterprise.

Innovation of Ideological and Political Education in Colleges and Universities from the Perspective of Network Public Opinion

In today's era of rapid development of network media,network public opinion has brought great changes to people's lives.As a place of frequent network public opinion,colleges and universities create a more complex and changeable network environment.This is both an opportunity and a challenge to the ideological and political education in universities.Therefore,through a brief introduction of the connotation and characteristics of network public opinion in colleges and universities,this article explores the innovation of ideological and political education in the context of network public opinion in terms of educational concepts,contents,and methods to ensure that this education plays a positive role in the new era.

Public Sentiment Analysis of Social Security Emergencies Based on Feature Fusion Model of BERT and TextLevelGCN

At present, the emotion classification method of Weibo public opinions based on graph neural network cannot solve the polysemy problem well, and the scale of global graph with fixed weight is too large. This paper proposes a feature fusion network model Bert-TextLevelGCN based on BERT pre-training and improved TextGCN. On the one hand, Bert is introduced to obtain the initial vector input of graph neural network containing rich semantic features. On the other hand, the global graph connection window of traditional TextGCN is reduced to the text level, and the message propagation mechanism of global sharing is applied. Finally, the output vector of BERT and TextLevelGCN is fused by interpolation update method, and a more robust mapping of positive and negative sentiment classification of public opinion text of “Tangshan Barbecue Restaurant beating people” is obtained. In the context of the national anti-gang campaign, it is of great significance to accurately and efficiently analyze the emotional characteristics of public opinion in sudden social violence events with bad social impact, which is of great significance to improve the government’s public opinion warning and response ability to public opinion in sudden social security events. .

Research on Prediction of Sentiment Trend of Food Safety Public Opinion

Emotion has a nearly decisive role in behavior, which will directly affect netizens’ views on food safety public opinion events, thereby affecting the development direction of public opinion on the event, and it is of great significance for food safety network public opinion to predict emotional trends to do a good job in food safety network public opinion guidance. In this paper, the dynamic text representation method XLNet is used to generate word vectors with context-dependent dependencies to distribute the text information of food safety network public opinion. Then, the word vector is input into the CNN-BiLSTM network for local semantic feature and context semantic extraction. The attention mechanism is introduced to give different weights according to the importance of features, and the emotional tendency analysis is carried out. Based on sentiment analysis, sentiment value time series data is obtained, and a time series model is constructed to predict sentiment trends. The sentiment analysis model proposed in this paper can well classify the sentiment of food safety network public opinion, and the time series model has a good effect on the prediction of food safety network public opinion sentiment trend. .

Predicting Stock Closing Price with Stock Network Public Opinion Based on AdaBoost-AAFSA-Elman Model and CEEMDAN Algorithm

To solve low prediction accuracy of Elman in predicting stock closing price,the model of adaptive boosting(AdaBoost)-improved artificial fish swarm algorithm(AAFSA)-Elman based on complete ensemble em-pirical mode decomposition with adaptive noise(CEEMDAN)is proposed.By adding different white noise to the original data,CEEMDAN algorithm is used to decompose attributes serial selected by Boruta algorithm and text mining.To optimize the weight and threshold of Elman,self-adaption step length and view scope are used to improve artificial fish swarm algorithm(AFSA).AdaBoost algorithm is used to compose 5 weak AAFSA-Elman predictors into a strong predictor by continuous iteration.Experiments show that the mean absolute percentage error(MAPE)of AdaBoost-AAFSA-Elman model reduces from 4.9423%to 1.2338%.This study provides an experimental method for the prediction of stock closing price based on network public opinio.

Prediction of network public opinion based on bald eagle algorithm optimized radial basis function neural network

Purpose-In the new era of highly developed Internet information,the prediction of the development trend of network public opinion has a very important reference significance for monitoring and control of public opinion by relevant government departments.Design/methodology/approach-Aiming at the complex and nonlinear characteristics of the network public opinion,considering the accuracy and stability of the applicable model,a network public opinion prediction model based on the bald eagle algorithm optimized radial basis function neural network(BES-RBF)is proposed.Empirical research is conducted with Baidu indexes such as“COVID-19”,“Winter Olympic Games”,“The 100th Anniversary of the Founding of the Party”and“Aerospace”as samples of network public opinion.Findings-The experimental results show that the model proposed in this paper can better describe the development trend of different network public opinion information,has good stability in predictive performance and can provide a good decision-making reference for government public opinion control departments.Originality/value-A method for optimizing the central value,weight,width and other parameters of the radial basis function neural network with the bald eagle algorithm is given,and it is applied to network public opinion trend prediction.The example verifies that the prediction algorithm has higher accuracy and better stability.